A feature of interest in the development of the low energy Bluetooth standard is direction finding, where a Bluetooth device equipped with multiple antenna elements can identify the direction of arrival of a received signal. Algorithms for the estimation of signal arrival angles or direction have previously been proposed. For Bluetooth low energy (BLE) devices, it is however necessary that the algorithm be computationally efficient.
Direction finding (DF) methods enable a device to determine the angle of arrival (AoA) or angle of departure (AoD) of signals by exploiting the differences in the received signal across different antenna elements. These methods can be classified as either spectral-based algorithm such as Bartlett, MUSIC and Capon or parametric-based algorithms such as Maximum Likelihood (ML). The techniques differ in performance and computational complexity.
While the complexity of DF schemes is manageable for a number of scenarios, for future low energy, low complexity devices with limited processing capabilities, the existing methods may not be feasible. This problem is exacerbated in 3D DF where the elevation angles also need to be computed.
Most of the conventional AoA/AoD algorithms in the array processing literature do not exploit the waveforms of the transmit signal. From the received signals at multiple antennas across several samples the sample covariance matrix may be derived. This covariance matrix is then used to derive a ‘spectrum’ wherein peaks correspond to the angles of interest.